Damle, Abhishek Priyadarshan2022-08-052022-08-052022-08-04vt_gsexam:35384http://hdl.handle.net/10919/111469Edge intelligence can reduce power dissipation to enable power-hungry long-range wireless applications. This work applies edge intelligence to quantify the reduction in power dissipation. We designed a wireless sensor node with a LoRa radio and implemented a decision tree classifier, in situ, to classify behaviors of cattle. We estimate that employing edge intelligence on our wireless sensor node reduces its average power dissipation by up to a factor of 50, from 20.10 mW to 0.41 mW. We also observe that edge intelligence increases the link budget without significantly affecting average power dissipation.ETDenIn CopyrightEdge intelligenceLoRaWANsmart farmcattle behaviordecision tree classifierThesis